Dataset to explore factors affecting COVID-19 vaccination intention. Evidence from Morocco

The coronavirus pandemic (COVID-19) has had an immense impact on humanity in every aspect of life. Governments around the world have mandated movement restrictions, including in the Moroccan government, in which unfortunately the pandemic continues to propagate and causes serious problems for public health and economic activities in the Kingdom. As a major factor in the fight against the spread of COVID-19, the Moroccan government has undertaken major efforts to ensure the availability of the COVID-19 vaccines for all citizens. These valuable efforts resulted in initiation of the vaccination campaign, which started on February 14, 2021. As vaccination was voluntary, identifying the factors promoting citizen's intention to take the vaccine against COVID-19 may help government to take additional precautions to address the propagation of COVID-19, and ensure a return to normal life. Hence, this data article aims to identify factors influencing the Moroccan citizens to get a vaccine for COVID-19. The data were collected using an online questionnaire among Moroccan citizens. In addition, the Partial Least Squares Structural Equation Modeling technique was adopted in order to analyze the collected dataset.


a b s t r a c t
The coronavirus pandemic (COVID-19) has had an immense impact on humanity in every aspect of life. Governments around the world have mandated movement restrictions, including in the Moroccan government, in which unfortunately the pandemic continues to propagate and causes serious problems for public health and economic activities in the Kingdom. As a major factor in the fight against the spread of COVID-19, the Moroccan government has undertaken major effort s to ensure the availability of the COVID-19 vaccines for all citizens. These valuable effort s resulted in initiation of the vaccination campaign, which started on February 14, 2021. As vaccination was voluntary, identifying the factors promoting citizen's intention to take the vaccine against COVID-19 may help government to take additional precautions to address the propagation of COVID-19, and ensure a return to normal life. Hence, this data article aims to identify factors influencing the Moroccan citizens to get a vaccine for COVID-19. The data were collected using an online questionnaire among Moroccan citizens. In addition, the Partial Least Squares Structural Equation Modeling technique was adopted in order to analyze the collected dataset.

Value of the Data
• The dataset can be used to identify factors affecting COVID-19 vaccination intention.
• This dataset provides insights into diverse aspects of the health belief model and theory of planned behaviour. • This dataset can be employed to enlighten public authorities (Moroccan Ministry of Health) on the importance of the health belief model variables and psychological antecedents of vaccination as key factors to enhance attitude toward vaccination and COVID-19 vaccination intention. • This dataset can serve as a valuable guideline for identifying factors that drive intent to vaccinate against COVID-19 in developing countries with similar socio-cultural and economic factors to Morocco.

Data Description
The measuring scales included in the current data article were selected from prior studies. Accordingly, the constructs of health belief model constructs [1] , were gauged through four subcomponents, namely perceived susceptibility (2-items), perceived severity (2-items), perceived benefits (3-items), and perceived barriers (5-items). Likewise, the 5 C psychological antecedents of vaccination were scored using 14 items [1] , including confidence (3-items), constraints (3items), complacency (3-items), calculation (3-items), and collective responsibility (2 items). Regarding operationalization of the latent constructs of theory of planned behaviour [1 , 2] , we selected 3 items for measuring attitude toward COVID-19 vaccine, 2 items to measure subjective norm (2-items), and a single item to operationalize both behavioral control and vaccination intention ( Table 1 ). The selection of these measurement scales is motivated by their empirical reliability, as they have been frequently applied in a variety of research contexts. Survey participants were instructed to rate items according to a 5-point Likert scale, which ranged from strongly disagree to strongly agree.

HBM-Sus2
Perceived severity I will be very sick if I get infected by COVID-19. HBM-Sev1 I am very concerned that I could die from COVID-19.
HBM-Sev2 Perceived benefits I think vaccination is good because it will make me less worried about COVID-19.

HBM-Ben1
I believe vaccination will decrease my risk of getting infected by COVID-19.

HBM-Ben2
I think the complications of COVID-19 will decrease if I get vaccinated and then get infected with Coronavirus.

HBM-Ben3
Perceived barriers I am worried that the possible side effects of the COVID-19 vaccination would interfere with my usual activities.

HBM-Bar1
I am concerned about the efficacy of the COVID-19 vaccine. HBM-Bar2 I have a concern that I may receive faulty/fake COVID-19 vaccine.

HBM-Bar3
It concerns me that the development of a COVID-19 vaccine is too rushed to test its safety properly.

HBM-Bar4
I am concerned about the long-term side effects of the COVID-19 vaccination.

HBM-Bar5
5C psychological antecedents of vaccination Confidence I am completely confident that COVID-19 vaccines are safe. PAV-Conf1 I am completely confident that COVID-19 vaccines are effective.

PAV-Conf2
Regarding COVID-19 vaccines, I am confident that public authorities decide in the best interest of the community.

PAV-Conf3
Constraints Everyday work stress may prevent me from getting vaccinated.

PAV-Cons1
For me, it is inconvenient to receive vaccinations. PAV-Cons2 Visiting the doctors makes me feel uncomfortable; this keeps me from getting vaccinated.

PAV-Cons3
Complacency I think it is unnecessary to receive vaccinations, as it cannot prevent COVID-19.

PAV-Com1
I believe my immune system is powerful; it will protect me from COVID-19.

PAV-Com2
I believe COVID-19 is not much a severe disease that I should get vaccinated against it.

PAV-Com3
Calculation When I think about getting vaccinated against COVID-19, I weigh the benefits and risks to make the best decision possible.

PAV-Cal1
When I think about getting vaccinated against COVID-19, I will first consider whether it is effective or not.

PAV-Cal2
Before I get COVID-19 vaccinated, I need to know about this vaccine in detail.

PAV-Cal3
Collective responsibility I will take COVID-19 vaccine because, in that way, I can protect people with a weaker immune system.

PAV-Res1
I think vaccination against COVID-19 is a collective action to prevent the spread of diseases.

PAV-Res2
Theory of Planned Behaviour Attitude toward COVID-19 vaccine I think the COVID-19 vaccination is necessary. ATCov1 I think the COVID-19 vaccination is a good idea.
ATCov2 I think the COVID-19 vaccination is beneficial. ATCov3 Subjective norm My family members will support me to get vaccinated against COVID-19.

SN1
People whose opinion I care about would say that it is a good idea for me to get vaccinated against COVID-19.

SN2
Behavioral control If I want, I can register for COVID-19 vaccination. PBC1
Given that science provides a valuable benefit to the economy of developing countries [3] , the purpose of this dataset is to identify motivating factors for citizens to be vaccinated against COVID-19, which will help to guide policymakers' future effort s to proactively develop vaccination programs for increasing vaccination intention among Moroccans citizens. Experts call for an interdisciplinary strategy for absorbing and filtering information to help understand factors influencing vaccine hesitancy [4] . At this level, this dataset aims to identify factors that may reduce vaccine hesitancy, by considering the theory of planned behaviour (TPB), the health belief model (HBM), and the 5c psychological antecedents of vaccination.

Experimental Design, Materials and Methods
Our research model was developed using the HBM, the 5 C psychological antecedents of vaccination and the TPB. As indicated in Fig. 1 , the health belief model including perceived susceptibility (H1), perceived severity (H2), perceived benefits (H3), and perceived barriers (H4) directly affects individual attitudes toward COVID-19 vaccination. In addition, the 5 C psychological antecedents of vaccination, i.e., confidence (H5), constraints (H6), complacency (H7), calculation (H8), and collective responsibility (H9) directly and significantly influence on attitudes toward COVID-19 vaccination. Likewise, the conceptual model assumes that individual attitude toward vaccination depends on subjective norm (H10). Further, attitude toward vaccination positively influences on perceived behavioural control (H11). These three variables, i.e., attitude (H12), subjective norm (H13), and perceived behavioural control (H14) are assumed to have a direct and positive impact on people's intention to be immunized against COVID-19.
The survey is designed around two parts. The first part is focused on gathering data on citizens' socio-demographic profiles, including gender, age, marital status, education level, occupation, city, monthly income, and vaccine preference. The second part of the questionnaire is designed to capture data related to latent variables including perceived susceptibility, perceived severity, perceived benefits, perceived barriers, confidence, constraints, complacency, calculation, collective responsibility, attitude toward COVID-19 vaccine, subjective norms, behavioral control, and vaccination intention.
To improve the comprehensibility of the survey questions, the developed questionnaire was pre-tested among ten government employees. The original questionnaire (English version) has been translated into Arabic and French by language specialists. Hence, respondents were allowed to select one of the three languages i.e., English, Arabic, or French to fill in the questionnaire.
The questionnaire has been administered through Google Forms, as well as the questionnaire link has been shared using social networks, including Telegram, Facebook, and WhatsApp. Hence, the survey was conducted through an online process among Moroccan citizens over a period of six months (From May to October 2021) and a total of, 323 valid responses have been obtained.
The gathered dataset was handled through Microsoft Excel in order to generate descriptive statistics on socio-demographic profiles of survey participants. While the collected data for the different latent variables were stored and coded in a comma-separated values (CSV) file which is compatible with the SmartPLS software. To verify the research model, we first operationalized the latent constructs (1), then elaborated and pretested the research questionnaire (2), followed by assembling data (3), and finally analyzing the collected dataset based on the structural equation modeling based on a partial least squares (PLS-SEM) approach using the SmartPLS program. At this level, the PLS-SEM approach was employed in order to verify the hypotheses.
As indicated in Fig. 2 , this approach required two complementary steps: the evaluation of the measurement models, and the validation of the structural model [5] . More accurately, the measurement models' assessment involves the checking of convergent validity across loading, Cronbach's alpha, composite reliability, and average variance extracted, whereas the checking of discriminant validity is based on the Fornell-Larcker and the Heterotrait-Monotrait Ratio (HTMT) criteria. Moreover, evaluating the inner model entails verifying the coefficient of determination, the effect size, the predictive relevance, and model goodness-of-fit [6] .
As shown in Table 3 , all items loading values are significantly higher than 0.71, which meet the scientific standards [7] . In addition, the values of Cronbach's alpha ( α), reliability ( ρA), composite reliability ( ρc), and average variance extracted (AVE) are respectively higher than 0.74, 0.84, 0.85, and 0.65. Table 4 demonstrates the discriminant validity of outer models based on the Fornell-Larcker criterion, showing that the AVE of each latent construct is higher than the construct's highest squared correlation with any other latent construct [8] .  As an alternative to Fornell-Larcker criterion, the HTMT offers a better assessment criterion to verify outer models' discriminant validity. Accordingly, the higher HTMT value was 0.82 ( Table 5 ), which meet the specialist recommendations [9] .
Furthermore, discriminant validity was verified by applying the cross-loading criterion ( Table 6 ), showing that an indicator's load on its allocated latent variable is greater than its loads on all other variables. Fig. 3 displays the values of the coefficient of determination (R 2 ), indicating that the R-square values of attitude toward COVID-19 vaccination, PBC, and COVID-19 vaccination intention are 0.72, 0.17, and 0.75, respectively. Table 7 provides the effect of size values of exogenous constructs on endogenous constructs. Further, for all endogenous constructs the Q square value of PBC, attitude toward COVD-19 vaccines, and vaccination intention are higher than zero, being 0.16, 0.64, and 0.73, indicating an acceptable predictive relevance of the model [10] ( Table 8 ). Moreover, the goodness of fit value is 0.67, reflecting a high goodness of fit of the model.
As detailed in Table 9 , the data analysis provides evidence that, from the health belief model,  ( Fig. 4 ).     Collective Responsibility → Attitude toward vaccine 0.

Funding Resources
This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships, which have or could be perceived to have influenced the work reported in this article.